Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features

ArXi:2603.03096v2 Announce Type: replace-cross How do speech models trained through self-supervised learning structure their representations? Previous studies have looked at how information is encoded in feature vectors across different layers. But few studies have considered whether speech characteristics are captured within individual dimensions of SSL features. In this paper we specifically look at speaker information using PCA on utterance-averaged representations.